Search

It has been a first year full of unexpected challenges for the new officers of SIGAI! Along with the election of a new leadership team, we began the year with many changes, including integrating a new leadership team, changes in several of the appointed officers, and of course the global pandemic which has radically altered many of our activities! Of particular note, we are excited to welcome on board Louise Dennis as the new conference coordination officer, Anuj Karpatne as a new co-editor for AI Matters, and Alan Tsang as the new information officer, taking over from Michael Rovatsos, Amy McGovern, and Hang Ma respectively. We are very grateful to Michael, Amy, and Hang for years of excellent service to SIGAI! While we were working on several new initiatives, like everyone else, the Covid-19 pandemic changed the nature of what we were able to do and what we had to focus on. Nevertheless, we managed to get several of these initiatives off the ground, and were lucky in that SIGAI was well-positioned both financially and in terms of our rapport with many communities.

The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Using a consensus-based expert elicitation process, we find that AI can enable the accomplishment of 134 targets across all the goals, but it may also inhibit 59 targets. However, current research foci overlook important aspects. The fast development of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable sustainable development. Failure to do so could result in gaps in transparency, safety, and ethical standards., Artificial intelligence (AI) is becoming more and more common in people’s lives. Here, the authors use an expert elicitation method to understand how AI may affect the achievement of the Sustainable Development Goals.

A longstanding barrier to deploying robots in the real world is the ongoing need to author robot behavior. Remote data collection–particularly crowdsourcing—is increasingly receiving interest. In this paper, we make the argument to scale robot programming to the crowd and present an initial investigation of the feasibility of this proposed method. Using an off-the-shelf visual programming interface, non-experts created simple robot programs for two typical robot tasks (navigation and pick-and-place). Each needed four subtasks with an increasing number of programming statements (if statement, while loop, variables) for successful completion of the programs. Initial findings of an online study (N = 279) indicate that non-experts, after minimal instruction, were able to create simple programs using an off-the-shelf visual programming interface. We discuss our findings and identify future avenues for this line of research.

The final publication is available at ACM via http://dx.doi.org/10.1145/3459990.3460719 Machine learning systems have become ubiquitous into our society. This has raised concerns about the potential discrimination that these systems might exert due to unconscious bias present in the data, for example regarding gender and race. Whilst this issue has been proposed as an essential subject to be included in the new AI curricula for schools, research has shown that it is a difficult topic to grasp by students. We propose an educational platform tailored to raise the awareness of gender bias in supervised learning, with the novelty of using Grad-CAM as an explainability technique that enables the classifier to visually explain its own predictions. Our study demonstrates that preadolescents (N=78, age 10-14) significantly improve their understanding of the concept of bias in terms of gender discrimination, increasing their ability to recognize biased predictions when they interact with the interpretable model, highlighting its suitability for educational programs. Peer Reviewed Objectius de Desenvolupament Sostenible::4 - Educació de Qualitat::4.4 - Per a 2030, augmentar substancialment el nombre de joves i persones adultes que tenen les competències necessàries, en particular tècniques i professionals, per a accedir a l’ocupació, el treball digne i l’emprenedoria Objectius de Desenvolupament Sostenible::4 - Educació de Qualitat

The purpose of this workshop is to help researchers develop methodological skills, especially in areas that are relatively new to them. With HRI researchers coming from diverse backgrounds in computer science, engineering, informatics, philosophy, psychology, and more disciplines, we can't be expert in everything. In this workshop, participants will be grouped with a mentor to enhance their study design and interdisciplinary work. Participants will submit 4-page papers with a small introduction and detailed method section for a project currently in the design process. In small groups led by a mentor in the area, they will discuss their method and obtain feedback. The workshop will include time to edit and improve the study. Workshop mentors include Drs. Cindy Bethel, Hung Hsuan Huang, Selma Sabanovic, Brian Scassellati, Megan Strait, Komatsu Takanori, Leila Takayama, and Ewart de Visser, with expertise in areas of real-world study, empirical lab study, questionnaire design, interview, participatory design, and statistics.

In this study, non-verbal behavior in diversely-skilled groups was observed while participating in a collaborative educational game with a humanoid robot. Research has indicated that a mediating robot gaze can equalize the verbal contributions from each differently skilled participant, promoting inclusion and learning. The experiment results were further analyzed, extending to non-verbal effects. The initial results from two experiments under different robot gaze behavior indicate that modifications in the robot gaze can lead to different gaze behavior in participants. It was observed that a gaze mediating behavior in the robot led to increased gaze change frequency among participants as well as more time spent mirroring the robot's gaze. These initial results show promise in how a robot can balance attention in a collaborative learning environment.

Inspired by the recent UNESCO report "I'd Blush if I Could", we tackle some of the issues regarding gendered AI through exploring the impact of feminist social robot behaviour on human-robot interaction. Specifically we consider (i) use of a social robot to encourage girls to consider studying robotics (and expression of feminist sentiment in this context), (ii) if/how robots should respond to abusive, and anti-feminist sentiment and (iii) how ('female') robots can be designed to challenge current gender-based norms of expected behaviour. We demonstrate that whilst there are complex interactions between robot, user and observer gender, we were able to increase girls' perceptions of robot credibility and reduce gender bias in boys. We suggest our work provides positive evidence for going against current digital assistant/traditional human gender-based norms, and the future role robots might have in reducing our gender biases.

Driving styles play a major role in the acceptance and use of autonomous vehicles. Yet, existing motion planning techniques can often only incorporate simple driving styles that are modeled by the developers of the planner and not tailored to the passenger. We present a new approach to encode human driving styles through the use of signal temporal logic and its robustness metrics. Specifically, we use a penalty structure that can be used in many motion planning frameworks, and calibrate its parameters to model different automated driving styles. We combine this penalty structure with a set of signal temporal logic formula, based on the Responsibility-Sensitive Safety model, to generate trajectories that we expected to correlate with three different driving styles: aggressive, neutral, and defensive. An online study showed that people perceived different parameterizations of the motion planner as unique driving styles, and that most people tend to prefer a more defensive automated driving style, which correlated to their self-reported driving style. QC 20220502Part of proceedings: ISBN 978-1-7281-9077-8

Multisensory integration influences emotional perception, as the McGurk effect demonstrates for the communication between humans. Human physiology implicitly links the production of visual features with other modes like the audio channel: Face muscles responsible for a smiling face also stretch the vocal cords that results in a characteristic smiling voice. For artificial agents capable of multimodal expression, this linkage is modeled explicitly. In our study, we observe the influence of visual and audio channel on the perception of the agent’s emotional state. We created two virtual characters to control for anthropomorphic appearance. We record videos of these agents either with matching or mismatching emotional expression in the audio and visual channel. In an online study we measured the agent’s perceived valence and arousal. Our results show that a matched smiling voice and smiling face increase both dimensions of the Circumplex model of emotions: ratings of valence and arousal grow. When the channels present conflicting information, any type of smiling results in higher arousal rating, but only the visual channel increases the perceived valence. When engineers are constrained in their design choices, we suggest they should give precedence to convey the artificial agent’s emotional state through the visual channel. Part of Proceedings: ISBN 978-1-6654-0019-0QC 20211026

The increasing use of robots in real-world applications will inevitably cause users to encounter more failures in interactions. While there is a longstanding effort in bringing human-likeness to robots, how robot embodiment affects users' perception of failures remains largely unexplored. In this paper, we extend prior work on robot failures by assessing the impact that embodiment and failure severity have on people's behaviours and their perception of robots. Our findings show that when using a smart-speaker embodiment, failures negatively affect users' intention to frequently interact with the device, however not when using a human-like robot embodiment. Additionally, users significantly rate the human-like robot higher in terms of perceived intelligence and social presence. Our results further suggest that in higher severity situations, human-likeness is distracting and detrimental to the interaction. Drawing on quantitative findings, we discuss benefits and drawbacks of embodiment in robot failures that occur in guided tasks.

During speech, people spontaneously gesticulate, which plays a key role in conveying information. Similarly, realistic co-speech gestures are crucial to enable natural and smooth interactions with social agents. Current end-to-end co-speech gesture generation systems use a single modality for representing speech: either audio or text. These systems are therefore confined to producing either acoustically-linked beat gestures or semantically-linked gesticulation (e.g., raising a hand when saying "high"): they cannot appropriately learn to generate both gesture types. We present a model designed to produce arbitrary beat and semantic gestures together. Our deep-learning based model takes both acoustic and semantic representations of speech as input, and generates gestures as a sequence of joint angle rotations as output. The resulting gestures can be applied to both virtual agents and humanoid robots. Subjective and objective evaluations confirm the success of our approach. The code and video are available at the project page https://svito-zar.github.io/gesticulator ., Comment: ICMI 2020 Best Paper Award. Code is available. 9 pages, 6 figures

We are happy to present the annual activity report of the ACM Special Interest Group on AI (ACM SIGAI), covering the period from July 2018 to June 2019. The scope of ACM SIGAI consists of the study of intelligence and its realization in computer systems (see also its web-site at sigai.acm.org). This includes areas such as autonomous agents, cognitive modeling, computer vision, constraint programming, human language technologies, intelligent user interfaces, knowledge discovery, knowledge representation and reasoning, machine learning, planning and search, problem solving and robotics. Members come from academia, industry and government agencies worldwide. ACM SIGAI recently added two new ACM SIGAI chapters, namely one professional chapter in Laguna Nigel (USA) and one student chapter at the SRM Institute of Science & Technology in Chennai. ACM SIGAI also added two new officers this year to be able to serve its membership better, namely Iolanda Leite from the Royal Institute of Technology (Sweden) as second newsletter editor-in-chief and Marion Neumann from Washington University in St. Louis (USA) as diversity officer, thus increasing diversity in the ACM SIGAI leadership committee by increasing both the number of international officers and the number of female officers and also furthering the internationalization of the ACM SIGAI newsletter. Marion will be covering diversity also as part of the ACM SIGAI newsletter. ACM SIGAI started officers meetings at major AI conferences already in 2018 and continued the new practice in 2019 (so far at the AAAI Conference), in addition to all-officers teleconferences and a monthly ACM SIGAI leadership teleconference.

When the goal is entertainment, designing language-based interactions between characters and small groups of young children is a balancing act. On the one hand, an autonomous character should support the freedom of expression and natural behaviors of children having fun. On the other hand, an autonomous character is only capable of supporting the activity it’s designed for and the behavior it anticipates. In the last five years we have watched this tension between freedom and constraint play out in hundreds of small groups in a variety of activities. Using two of the activities as examples, we chart the ups and downs of turn-taking and other language behaviors along the Fun Curve.

Welcome to the second issue of the sixth volume of the AI Matters Newsletter. We open with the annual report of SIGAI by our new leaership team. We then bring you a brief report on upcoming SIGAI Events and Conference Reports by Louise Dennis, our new conference coordination officer. In our regular Education column, Todd Neller presents a retrospective of an initiative for mentoring undergraduate research in AI that has been going on since 2014. In the Policy column, Larry Medsker covers ongoing discussions on AI policy issues, for example with respect to ethical implications on Facial Recognition software and surveillance technologies to track the coronavirus pandemic. We end the issue with our regular column on AI generated crosswords from Adi Botea.

Welcome to the fourth issue of the fifth volume of the AI Matters Newsletter. This issue is full of great new articles and stories! In our regular articles, we open with an event report by Louise Dennis on recent conferences relevant to SIGAI organized around the world. Louise Dennis then provides a summary of upcoming AI events. In the policy column, Larry Medsker covers recent policies on national strategy for AI laid down by the U.S. government, implications of AI on workplace transitions, President Trump's executive order on re-establishing the President's Council of Advisors on Science and Technology (PCAST), and more. Our final regular column is our AI crosswords from Adi Botea. Enjoy! We have three contributed articles in our current issue. In the first article, Sharad Sinha and Clint George provide a summary of the events and activities of a recent workshop conducted in India on AI for high school students. In the second article, Trevor Watkins describes the Cosmology of Artificial Intelligence Project (CAIP) that uses innovative visualization techniques to present the evolution of the field of AI. In the third article, Virginia Dignum discusses how AI is progressively trending to become a multi-disciplinary field of reseach and the implications of this trend on the future of AI.

This paper explores the role of prosocial behaviour when people team up with robots in a collaborative game that presents a social dilemma similar to a public goods game. An experiment was conducted with the proposed game in which each participant joined a team with a prosocial robot and a selfish robot. During 5 rounds of the game, each player chooses between contributing to the team goal (cooperate) or contributing to his individual goal (defect). The prosociality level of the robots only affects their strategies to play the game, as one always cooperates and the other always defects. We conducted a user study at the office of a large corporation with 70 participants where we manipulated the game result (winning or losing) in a between-subjects design. Results revealed two important considerations: (1) the prosocial robot was rated more positively in terms of its social attributes than the selfish robot, regardless of the game result; (2) the perception of competence, the responsibility attribution (blame/credit), and the preference for a future partner revealed significant differences only in the losing condition. These results yield important concerns for the creation of robotic partners, the understanding of group dynamics and, from a more general perspective, the promotion of a prosocial society.

Virtual Reality (VR) can greatly benefit Human-Robot Interaction (HRI) as a tool to effectively iterate across robot designs. However, possible system limitations of VR could influence the results such that they do not fully reflect real-life encounters with robots. In order to better deploy VR in HRI, we need to establish a basic understanding of what the differences are between HRI studies in the real world and in VR. This paper investigates the differences between the real life and VR with a focus on proxemic preferences, in combination with exploring the effects of visual familiarity and spatial sound within the VR experience. Results suggested that people prefer closer interaction distances with a real, physical robot than with a virtual robot in VR. Additionally, the virtual robot was perceived as more discomforting than the real robot, which could result in the differences in proxemics. Overall, these results indicate that the perception of the robot has to be evaluated before the interaction can be studied. However, the results also suggested that VR settings with different visual familiarities are consistent with each other in how they affect HRI proxemics and virtual robot perceptions, indicating the freedom to study HRI in various scenarios in VR. The effect of spatial sound in VR drew a more complex picture and thus calls for more in-depth research to understand its influence on HRI in VR.

For practical reasons, most human-robot interaction (HRI) studies focus on short-term interactions between humans and robots. However, such studies do not capture the difficulty of sustaining engagement and interaction quality across long-term interactions. Many real-world robot applications will require repeated interactions and relationship-building over the long term, and personalization and adaptation to users will be necessary to maintain user engagement and to build rapport and trust between the user and the robot. This full-day workshop brings together perspectives from a variety of research areas, including companion robots, elderly care, and educational robots, in order to provide a forum for sharing and discussing innovations, experiences, works-in-progress, and best practices which address the challenges of personalization in long-term HRI.

Many people who interact with robots in the near future will not have prior experience, and they are likely to intuitively form their first impressions of the robot based on its appearance. This paper explores the effects of component material on people’s perception of the robots in terms of social attributes and willingness to interact. Participants watched videos of three robots with different outer materials: wood, synthetic fur, and plastic. The results showed that people rated the perceived warmth of a plastic robot lower than a wooden or furry robot. Ratings of perceived competence and discomfort did not differ between the three robots. QC 20191122

In collaborative tasks, people rely both on verbal and non-verbal cues simultaneously to communicate with each other. For human-robot interaction to run smoothly and naturally, a robot should be equipped with the ability to robustly disambiguate referring expressions. In this work, we propose a model that can disambiguate multimodal fetching requests using modalities such as head movements, hand gestures, and speech. We analysed the acquired data from mixed reality experiments and formulated a hypothesis that modelling temporal dependencies of events in these three modalities increases the model’s predictive power. We evaluated our model on a Bayesian framework to interpret referring expressions with and without exploiting the temporal prior.

For effective verbal communication in collaborative tasks, robots need to account for the different perspectives of their human partners when referring to objects in a shared space. For example, when a robot helps its partner find correct pieces while assembling furniture, it needs to understand how its collaborator perceives the world and refer to objects accordingly. In this work, we propose a method to endow robots with perspective-taking abilities while spatially referring to objects. To examine the impact of our proposed method, we report the results of a user study showing that when the objects are spatially described from the users’ perspectives, participants take less time to find the referred objects, find the correct objects more often and consider the task easier.

Most of the motion re-targeting techniques are grounded on virtual character animation research, which means that they typically assume that the target embodiment has unconstrained joint angular velocities. However, because robots often do have such constraints, traditional re-targeting approaches can originate irregular delays in the robot motion. With the goal of ensuring synchronization between verbal and nonverbal behavior, this paper proposes an optimization framework for processing re-targeted motion sequences that addresses constraints such as joint angle and angular velocities. The proposed framework was evaluated on a humanoid robot using both objective and subjective metrics. While the analysis of the joint motion trajectories provides evidence that our framework successfully performs the desired modifications to ensure verbal and nonverbal behavior synchronization, results from a perceptual study showed that participants found the robot motion generated by our method more natural, elegant and lifelike than a control condition.

Picking up objects requested by a human user is a common task in human-robot interaction. When multiple objects match the user's verbal description, the robot needs to clarify which object the user is referring to before executing the action. Previous research has focused on perceiving user's multimodal behaviour to complement verbal commands or minimising the number of follow up questions to reduce task time. In this paper, we propose a system for reference disambiguation based on visualisation and compare three methods to disambiguate natural language instructions. In a controlled experiment with a YuMi robot, we investigated realtime augmentations of the workspace in three conditions - head-mounted display, projector, and a monitor as the baseline - using objective measures such as time and accuracy, and subjective measures like engagement, immersion, and display interference. Significant differences were found in accuracy and engagement between the conditions, but no differences were found in task time. Despite the higher error rates in the head-mounted display condition, participants found that modality more engaging than the other two, but overall showed preference for the projector condition over the monitor and head-mounted display conditions.

Welcome to the fourth issue of the fourth volume of the AI Matters Newsletter! Our biggest news is that we would like to extend a big welcome to our new co-editor! Iolanda Leite is an Assistant Professor at the School of Computer Science and Electrical Engineering at the KTH Royal Institute of Technology in Sweden. Iolanda is excited to join the AI Matters team and to help to continue to grow the newsletter for ACM SIGAI. She is also our guest for the interview this month so please go to our interview and learn more about our new co-editor!

We designed an augmented reality interface for dialog that enables the control of multimodal behaviors in telepresence robot applications. This interface, when paired with a telepresence robot, enables a single operator to accurately control and coordinate the robot's verbal and nonverbal behaviors. Depending on the complexity of the desired interaction, however, some applications might benefit from having multiple operators control different interaction modalities. As such, our interface can be used by either a single operator or pair of operators. In the paired-operator system, one operator controls verbal behaviors while the other controls nonverbal behaviors. A within-subjects user study was conducted to assess the usefulness and validity of our interface in both single and paired-operator setups. When faced with hard tasks, coordination between verbal and nonverbal behavior improves in the single-operator condition. Despite single operators being slower to produce verbal responses, verbal error rates were unaffected by our conditions. Finally, significantly improved presence measures such as mental immersion, sensory engagement, ability to view and understand the dialog partner, and degree of emotion occur for single operators that control both the verbal and nonverbal behaviors of the robot.

Role-play scenarios have been considered a successful learning space for children to develop their social and emotional abilities. In this paper, we investigate whether socially assistive robots in role-playing settings are as effective with small groups of children as they are with a single child, and whether individual factors such as gender, grade level (first vs. second), perception of the robots (peer vs. adult), and empathy level (low vs. high) play a role in these two interaction contexts. We conducted a three-week repeated exposure experiment where 40 children interacted with socially assistive robotic characters that acted out interactive stories around words that contribute to expanding children's emotional vocabulary. Our results showed that although participants who interacted alone with the robots recalled the stories better than participants in the group condition, no significant differences were found in children's emotional interpretation of the narratives. With regard to individual differences, we found that a single child setting appeared more appropriate to first graders than a group setting, empathy level is an important predictor for emotional understanding of the narratives, and children's performance varies depending on their perception of the robots (peer vs. adult) in the two conditions.

Persistent memory is a critical mechanism in long-term human-robot interaction. In this work, we investigate how a robot can use information from prior conversations with the same child to foster a sense of relationship over time. To address this question, we conducted a repeated interaction study with three experimental conditions: a baseline control condition, in which the robot retains no information between conversations and relies on a typical elicitation-response paradigm; a persistence condition, in which children experience the same topic flow but with some robot turns that refer back to prior shared events; and a pro-active persistence condition, in which the robot attempts to offer its own feelings and opinions pro-actively and congruently with what it knows about the child. Our results indicate age differences with respect to the measures of interest. During conversations with the robot, older children who were assigned to the persistence conditions exhibited more positive affect, while younger children showed more positive affect in the control condition. Moreover, in a set of comparative judgments among robots they had played with, children in the augmented persistence condition considered PIPER to be the most intelligent and their favorite more often than children in the other conditions, overall, but the effect was more evident in the older children.

This article surveys the area of computational empathy, analysing different ways by which artificial agents can simulate and trigger empathy in their interactions with humans. Empathic agents can be seen as agents that have the capacity to place themselves into the position of a user’s or another agent’s emotional situation and respond appropriately. We also survey artificial agents that, by their design and behaviour, can lead users to respond emotionally as if they were experiencing the agent’s situation. In the course of this survey, we present the research conducted to date on empathic agents in light of the principles and mechanisms of empathy found in humans. We end by discussing some of the main challenges that this exciting area will be facing in the future.

Joint storytelling is a common parent-child activity and brings multiple benefits such as improved language learning for children. Most existing storytelling robots offer rigid interaction with children and do not contribute to children's stories. In this paper, we envision a robot that collaborates with a child to create oral stories in a highly interactive manner. We performed a Wizard-of-Oz feasibility study, which involved 78 children between 4 and 10 years old, to compare two collaboration strategies: (1) inserting new story content and relating it to the existing story and (2) inserting content without relating it to the existing story. We hypothesize the first strategy can foster true collaboration and create rapport, whereas the second is a safe strategy when the robot cannot understand the story. We observed that, although the first strategy creates heavier cognitive load, it was as enjoyable as the second. We also observed some indications that the first strategy may mitigate the difficulties in story creation for young children under the age of 7 and encourage children to speak more. This study suggests that a mixture strategy is feasible for robots in collaborative storytelling, providing sufficient cognitive challenge while concealing its shortcomings on natural language understanding. QC 20170823

Social perception abilities are among the most important skills necessary for robots to engage humans in natural forms of interaction. Affect-sensitive robots are more likely to be able to establish and maintain believable interactions over extended periods of time. Nevertheless, the integration of affect recognition frameworks in real-time human-robot interaction scenarios is still underexplored. In this article, we propose and evaluate a context-sensitive affect recognition framework for a robotic game companion for children. The robot can automatically detect affective states experienced by children in an interactive chess game scenario. The affect recognition framework is based on the automatic extraction of task features and social interaction-based features. Vision-based indicators of the children’s nonverbal behaviour are merged with contextual features related to the game and the interaction and given as input to support vector machines to create a context-sensitive multimodal system for affect recognition. The affect recognition framework is fully integrated in an architecture for adaptive human-robot interaction. Experimental evaluation showed that children’s affect can be successfully predicted using a combination of behavioural and contextual data related to the game and the interaction with the robot. It was found that contextual data alone can be used to successfully predict a subset of affective dimensions, such as interest toward the robot. Experiments also showed that engagement with the robot can be predicted using information about the user’s valence, interest and anticipatory behaviour. These results provide evidence that social engagement can be modelled as a state consisting of affect and attention components in the context of the interaction.

Welcome to the first issue of the fifth volume of the AI Matters Newsletter! This issue opens with some news on a new SIGAI Student Travel Scholarship where we aim to encourage students from traditionally underrepresented geographic locations to apply and attend SIGAI supported events. We also summarize the fourth AAAI/ACM SIGAI Job Fair, which continues to grow with the increasing popularity of AI. In our interview series, Marion Neumann interviews Tom Dietterich, an Emeritus Professor at Oregon State University and one of the pioneers in Machine Learning.

Content authoring of verbal and nonverbal behavior is a limiting factor when developing agents for repeated social interactions with the same user. We present PIP, an agent that crowdsources its own multimodal language behavior using a method we call semi-situated learning. PIP renders segments of its goal graph into brief stories that describe future situations, sends the stories to crowd workers who author and edit a single line of character dialog and its manner of expression, integrates the results into its goal state representation, and then uses the authored lines at similar moments in conversation. We present an initial case study in which the language needed to host a trivia game interaction is learned pre-deployment and tested in an autonomous system with 200 users "in the wild." The interaction data suggests that the method generates both meaningful content and variety of expression.

We have developed a toy sized humanoid robot with soft air-filled modules on its links which sense contact and protect the robot and any interacting humans from damaging collisions. This robot, meant for robust physical interaction, is required to endure contact with children in the form of hugs and other playful interactions. It is therefore necessary to quantify the forces exerted during these interactions so that robots can be designed to both withstand these forces, as well as interact safely and intuitively in these situations. To quantify the range of forces exerted by children when performing both soft and strong hugs, we conducted a study in which 28 children (11 boys, 17 girls) between 4 and 10 years old hugged a pressure sensing doll while the pressure was recorded. We found a child's maximum expected hugging force (2.623 psi for our setup) during free play. The data gathered in this study will guide the further development of our physically interactive robot.

As research on robotic tutors increases, it becomes more relevant to understand whether and how robots will be able to keep students engaged over time. In this paper, we propose an algorithm to monitor engagement in small groups of children and trigger disengagement repair interventions when necessary. We implemented this algorithm in a scenario where two robot actors play out interactive narratives around emotional words and conducted a field study where 72 children interacted with the robots three times in one of the following conditions: control (no disengagement repair), targeted (interventions addressing the child with the highest disengagement level) and general (interventions addressing the whole group). Surprisingly, children in the control condition had higher narrative recall than in the two experimental conditions, but no significant differences were found in the emotional interpretation of the narratives. When comparing the two different types of disengagement repair strategies, participants who received targeted interventions had higher story recall and emotional understanding, and their valence after disengagement repair interventions increased over time.

In their expanding role as tutors, home and healthcare assistants, robots must effectively interact with individuals of varying ability and temperament. Indeed, deploying robots in long-term social engagements will almost certainly require robots to reliably detect and adapt to changes in the demeanor of social partners to promote trust and more productive collaboration. However, the recognition of emotional state typically relies on the interpretation of very subtle cues, often varying from one person to the next. In addition, while facial expressions, body posture and features of speech have been used to detect affective changes, the robustness of these measures is often hindered by cultural and age differences. Recently, infrared thermography has shown promise in detecting guilt, fear and stress, indicating that it may be a viable sensing modality for improved human-robot interaction. In this study, we evaluated the efficacy of using a far infrared (FIR) camera for detecting robot-elicited affective response compared to video-elicited affective response by tracking thermal changes in five areas of the face. Further, we analyzed localized changes in the face to assess whether thermal and electrodermal responses to emotions elicited by traditional video techniques and by robots are similar. Finally, we performed principal component analysis to reduce the dimensionality of data and evaluated the performance using machine learning techniques for classifying thermal data by emotion state, resulting in a thermal classifier with a performance accuracy of 77.5%.

Affect sensitivity is an important requirement for artificial companions to be capable of engaging in social interaction with human users. This paper provides a general overview of some of the issues arising from the design of an affect recognition framework for artificial companions. Limitations and challenges are discussed with respect to other capabilities of companions and a real world scenario where an iCat robot plays chess with children is presented. In this scenario, affective states that a robot companion should be able to recognise are identified and the non-verbal behaviours that are affected by the occurrence of these states in the children are investigated. The experimental results aim to provide the foundation for the design of an affect recognition system for a game companion: in this interaction scenario children tend to look at the iCat and smile more when they experience a positive feeling and they are engaged with the iCat.

As social robots become more widespread in educational environments, their ability to understand group dynamics and engage multiple children in social interactions is crucial. Social dominance is a highly influential factor in social interactions, expressed through both verbal and nonverbal behaviors. In this paper, we present a method for determining whether a participant is high or low in social dominance in a group interaction with children and robots. We investigated the correlation between many verbal and nonverbal behavioral features with social dominance levels collected through teacher surveys. We additionally implemented Logistic Regression and Support Vector Machines models with classification accuracies of 81% and 89%, respectively, showing that using a small subset of nonverbal behavioral features, these models can successfully classify children's social dominance level. Our approach for classifying social dominance is novel not only for its application to children, but also for achieving high classification accuracies using a reduced set of nonverbal features that, in future work, can be automatically extracted with current sensing technology.

Robot assistive technology is becoming increasingly prevalent. Despite the growing body of research in this area, the role of type of interaction (i.e., small groups versus individual interactions) on effectiveness of interventions is still unclear. In this paper, we explore a new direction for socially assistive robotics, where multiple robotic characters interact with children in an interactive storytelling scenario. We conducted a between-subjects repeated interaction study where a single child or a group of three children interacted with the robots in an interactive narrative scenario. Results show that although the individual condition increased participant’s story recall abilities compared to the group condition, the emotional interpretation of the story content seemed more dependent on the difficulty level rather than the study condition. Our findings suggest that, despite the type of interaction, interactive narratives with multiple robots are a promising approach to foster children’s development of social-related skills.

In this paper, we describe the results of an interview study conducted across several European countries on teachers' views on the use of empathic robotic tutors in the classroom. The main goals of the study were to elicit teachers' thoughts on the integration of the robotic tutors in the daily school practice, understanding the main roles that these robots could play and gather teachers' main concerns about this type of technology. Teachers' concerns were much related to the fairness of access to the technology, robustness of the robot in students' hands and disruption of other classroom activities. They saw a role for the tutor in acting as an engaging tool for all, preferably in groups, and gathering information about students' learning progress without taking over the teachers' responsibility for the actual assessment. The implications of these results are discussed in relation to teacher acceptance of ubiquitous technologies in general and robots in particular.

Turn-taking decisions in multiparty settings are complex, especially when the participants are children. Our goal is to endow an interactive character with appropriate turn-taking behavior using visual, audio and contextual features. To that end, we investigate three distinct turn-taking models: a baseline model grounded in established turn-taking rules for adults and two machine learning models, one trained with data collected in situ and the other trained with data collected in more controlled conditions. The three models are shown to have different profiles of behavior during silences, overlapping speech, and at the end of participants' turns. An exploratory user evaluation focusing on the decision points where the models differ showed clear preference for the machine learning models over the baseline model. The results indicate that the rules for language interactions with small groups of children are not simply an extension of the rules for interacting with small groups of adults.

Serious Games rely on interactive systems to provide an efficient communication medium between the tutor and the user. Designing and implementing such medium is a multi-disciplinary task that aims at an environment that engages the user in a learning activity. User engagement is significantly related to the users' sense of immersion or his willingness to accept the reality proposed by a game environment. This is a very relevant research topic for Artificial Intelligence (AI), since it requires computational systems to generate believable behaviors that can promote the users' willingness to enter and engage in the game environment. In order to do this, AI research has been relying on social sciences, in particular psychology and sociology models, to ground the creation of computational models for non-player characters that behave according to the users' expectations. In this chapter, the authors present some of the most relevant NPC research contributions following this approach.

Language-based interfaces for children hold great promise in education, therapy, and entertainment. An important subset of these interfaces includes those with a virtual agent that mediates the interaction. When participants are groups of children, the agent will need to exert a certain amount of turn-taking control to ensure that all group members participate and benefit from the experience, but must do so without being so overtly directive as to undermine the children's enjoyment of and engagement in the task. We present a hierarchy of nonverbal and verbal behaviors that a virtual agent can employ flexibly when passing the conversational turn. When used effectively, these behaviors can equalize participation, and potentially decrease the amount of overlapping speech among participants, improving automatic speech recognition in turn. We evaluated the behaviors by having children play a language-based game twice, once with a flexible host and once with an inflexible host that did not have access to the behaviors. Post-game opinion cards revealed no difference between the conditions with respect to fun or likability of the host, despite the flexible agent eliciting more evenly distributed play.

In this paper we discuss how the EMOTE project will design, develop and evaluate a new generation of artificial embodied tutors that have perceptive capabilities to engage in empathic interactions with learners in a shared physical space.

As the field of HRI evolves, it is important to understand how users interact with robots over long periods. This paper reviews the current research on long-term interaction between users and social robots. We describe the main features of these robots and highlight the main findings of the existing long-term studies. We also present a set of directions for future research and discuss some open issues that should be addressed in this field.